Various noise reduction techniques are currently available to enhance speech containing background noise in a diversity of applications including those related to communication and control. One such technique is called Spectral Subtraction (See, “S. Boll, Suppression of acoustic noise in speech using spectral subtraction.” IEEE Transactions on Acoust. Speech and Signal Processing, Vol. 27, (1979) pp. 1109-1121.) This technique involves estimating the power spectrum of the noise and subtracting the estimated noise power spectrum from the speech plus noise power spectrum. This technique suffers from a problem called musical artifacts. Another technique involves estimation of the clean speech spectral magnitude from a noisy speech spectrum using an optimal minimum mean squared estimator based on the Ephraim and Malah algorithm (See Y. Ephraim and D. Malah, “Speech enhancement using optimal nonlinear spectral amplitude estimation,” in Proc. IEEE Int. Conf. Acoust. Speech Signal Processing (Boston), 1983, pp. 1118-1121. and Y. Ephraim and D. Malah, “Speech enhancement using a minimum means-square error log-spectral amplitude estimator,” IEEE Trans. Acoust. Speech Signal Processing, vol. ASSP-33, no. 2, pp. 443-445, 1985.). All of these techniques suffer from the problem that as the signal to noise ratio decreases (i.e. the noise power increases relative to the speech power), the enhanced speech sounds more unnatural and distorted. At some point, a listener might actually prefer to simply listen to the noisy speech rather than the badly distorted “enhanced” speech.
Therefore, there exists a need for improved systems and methods that reduce background noise for speech enhancement.